The present disclosure generally relates to eye tracking in virtual reality systems, and specifically relates to light-field waveguide integrated eye tracking.
For further development of artificial reality systems, eye tracking serves as a necessary technology advancement that can facilitate providing information related to user's interaction and gaze direction. With efficient implementation of eye tracking, artificial reality systems can focus on aspects that are directly related to a visual experience of an end-user. Based on information related to a position and orientation of a user's eye in an eye-box, a maximum pixel density (in a traditional display vernacular) may need to be provided only in a foveal region of the user's gaze, while a lower pixel resolution can be used in other regions leading to savings in power consumption and computing cycles. The resolution of pixel density can be reduced in non-foveal regions either gradually or in a step-wise fashion (e.g., by over an order of magnitude per each step).
Integrating eye tracking into a small form-factor package that maintains stability and calibration can be often challenging. Traditionally, eye tracking architectures are based on an image formation through the use of a “hot mirror”, or by utilizing devices that work based on substantially similar methods. When the “hot mirror” approach is employed, an imaging device (camera) receives light that reflects off the “hot mirror” to image a user's eye-box. The light was originally emitted by a (typically) infrared (IR) light source, e.g., IR light emitting diodes (LEDs) encircling the viewing optics. In the imaging approach, this provides a path for the camera to image the eye-box region of the device, which will allow one or more surfaces of a user's eye to be imaged and correlated to a gaze direction. The image formed by the camera may identify various features of the eye, including light reflected by any visible surface, such as the anterior and posterior corneal surfaces, the pupil, the iris, the sclera, and eyebrows, eyelashes, and other facial features. Internal structures may also be observed by the camera, including reflections from the retina or the anterior or posterior crystalline lens surfaces. Eye tracking algorithms typically use a model-based approach, where these features are identified and used to refine the model parameters and, correspondingly, estimate the state of the eye, including its position and orientation with respect to a head-mounted display. In an alternative configuration, the hot-mirror can also be used in a non-imaging configuration, avoiding the need to process and use image(s) of the one or more surfaces of the user's eye. This can be achieved, for example, based on correlating an eye-gaze coordinate with a maximized “red-eye” light signal, which is maximized around the foveal location due to the so-called “foveal reflex.”
However, implementing the hot-mirror based eye-tracking, whether imaging or non-imaging, into a small package that maintains stability and calibration is challenging. Therefore, more efficient methods for eye-tracking are desired for implementation in artificial reality systems.